Serum biomarker-based early detection of pancreatic ductal adenocarcinomas with ensemble learning.

BACKGROUND: Earlier detection of pancreatic ductal adenocarcinoma (PDAC) is key to improving patient outcomes, as it is mostly detected at advanced stages which are associated with poor survival. Developing non-invasive blood tests for early detection would be an important breakthrough. METHODS: The primary objective of the work presented here is to use a dataset that is prospectively collected, to quantify a set of cancer-associated proteins and construct multi-marker models with the capacity to predict PDAC years before diagnosis. The data used is part of a nested case-control study within the UK Collaborative Trial of Ovarian Cancer Screening and is comprised of 218 samples, collected from a total of 143 post-menopausal women who were diagnosed with pancreatic cancer within 70 months after sample collection, and 249 matched non-cancer controls. We develop a stacked ensemble modelling technique to achieve robustness in predictions and, therefore, improve performance in newly collected datasets. RESULTS: Here we show that with ensemble learning we can predict PDAC status with an AUC of 0.91 (95% CI 0.75-1.0), sensitivity of 92% (95% CI 0.54-1.0) at 90% specificity, up to 1 year prior to diagnosis, and at an AUC of 0.85 (95% CI 0.74-0.93) up to 2 years prior to diagnosis (sensitivity of 61%, 95% CI 0.17-0.83, at 90% specificity). CONCLUSIONS: The ensemble modelling strategy explored here outperforms considerably biomarker combinations cited in the literature. Further developments in the selection of classifiers balancing performance and heterogeneity should further enhance the predictive capacity of the method.

Pancreatic cancers are most frequently detected at an advanced stage. This limits treatment options and contributes to the dismal survival rates currently recorded. The development of new tests that could improve detection of early-stage disease is fundamental to improve outcomes. Here, we use advanced data analysis techniques to devise an early detection test for pancreatic cancer. We use data on markers in the blood from people enrolled on a screening trial. Our test correctly identifies as positive for pancreatic cancer 91% of the time up to 1 year prior to diagnosis, and 78% of the time up to 2 years prior to diagnosis. These results surpass previously reported tests and should encourage further evaluation of the test in different populations, to see whether it should be adopted in the clinic.

Blood levels of adiponectin and IL-1Ra distinguish type 3c from type 2 diabetes: Implications for earlier pancreatic cancer detection in new-onset diabetes.

BACKGROUND: Screening for pancreatic ductal adenocarcinoma (PDAC) in populations at high risk is recommended. Individuals with new-onset type 2 diabetes mellitus (NOD) are the largest high-risk group for PDAC. To facilitate screening, we sought biomarkers capable of stratifying NOD subjects into those with type 2 diabetes mellitus (T2DM) and those with the less prevalent PDAC-related diabetes (PDAC-DM), a form of type 3c DM commonly misdiagnosed as T2DM. METHODS: Using mass spectrometry- and immunoassay-based methodologies in a multi-stage analysis of independent sample sets (n=443 samples), blood levels of 264 proteins were considered using Ingenuity Pathway Analysis, literature review and targeted training and validation. FINDINGS: Of 30 candidate biomarkers evaluated in up to four independent patient sets, 12 showed statistically significant differences in levels between PDAC-DM and T2DM. The combination of adiponectin and interleukin-1 receptor antagonist (IL-1Ra) showed strong diagnostic potential, (AUC of 0.91; 95% CI: 0.84-0.99) for the distinction of T3cDM from T2DM. INTERPRETATION: Adiponectin and IL-1Ra warrant further consideration for use in screening for PDAC in individuals newly-diagnosed with T2DM. FUNDING: North West Cancer Research, UK, Cancer Research UK, Pancreatic Cancer Action, UK.

Non-Histone Protein Methylation: Biological Significance and Bioengineering Potential.

Protein methylation is a key post-translational modification whose effects on gene expression have been intensively studied over the last two decades. Recently, renewed interest in non-histone protein methylation has gained momentum for its role in regulating important cellular processes and the activity of many proteins, including transcription factors, enzymes, and structural complexes. The extensive and dynamic role that protein methylation plays within the cell also highlights its potential for bioengineering applications. Indeed, while synthetic histone protein methylation has been extensively used to engineer gene expression, engineering of non-histone protein methylation has not been fully explored yet. Here, we report the latest findings, highlighting how non-histone protein methylation is fundamental for certain cellular functions and is implicated in disease, and review recent efforts in the engineering of protein methylation.

DNA methylation signatures to predict the cervicovaginal microbiome status.

BACKGROUND: The composition of the microbiome plays an important role in human health and disease. Whether there is a direct association between the cervicovaginal microbiome and the host's epigenome is largely unexplored. RESULTS: Here we analyzed a total of 448 cervicovaginal smear samples and studied both the DNA methylome of the host and the microbiome using the Illumina EPIC array and next-generation sequencing, respectively. We found that those CpGs that are hypo-methylated in samples with non-lactobacilli (O-type) dominating communities are strongly associated with gastrointestinal differentiation and that a signature consisting of 819 CpGs was able to discriminate lactobacilli-dominating (L-type) from O-type samples with an area under the receiver operator characteristic curve (AUC) of 0.84 (95% CI = 0.77-0.90) in an independent validation set. The performance found in samples with more than 50% epithelial cells was further improved (AUC 0.87) and in women younger than 50 years of age was even higher (AUC 0.91). In a subset of 96 women, the buccal but not the blood cell DNA showed the same trend as the cervicovaginal samples in discriminating women with L- from O-type cervicovaginal communities. CONCLUSIONS: These findings strongly support the view that the epithelial epigenome plays an essential role in hosting specific microbial communities.

Multi-Marker Longitudinal Algorithms Incorporating HE4 and CA125 in Ovarian Cancer Screening of Postmenopausal Women.

Longitudinal CA125 algorithms are the current basis of ovarian cancer screening. We report on longitudinal algorithms incorporating multiple markers. In the multimodal arm of United Kingdom Collaborative Trial of Ovarian Cancer Screening (UKCTOCS), 50,640 postmenopausal women underwent annual screening using a serum CA125 longitudinal algorithm. Women (cases) with invasive tubo-ovarian cancer (WHO 2014) following outcome review with stored annual serum samples donated in the 5 years preceding diagnosis were matched 1:1 to controls (no invasive tubo-ovarian cancer) in terms of the number of annual samples and age at randomisation. Blinded samples were assayed for serum human epididymis protein 4 (HE4), CA72-4 and anti-TP53 autoantibodies. Multimarker method of mean trends (MMT) longitudinal algorithms were developed using the assay results and trial CA125 values on the training set and evaluated in the blinded validation set. The study set comprised of 1363 (2-5 per woman) serial samples from 179 cases and 181 controls. In the validation set, area under the curve (AUC) and sensitivity of longitudinal CA125-MMT algorithm were 0.911 (0.871-0.952) and 90.5% (82.5-98.6%). None of the longitudinal multi-marker algorithms (CA125-HE4, CA125-HE4-CA72-4, CA125-HE4-CA72-4-anti-TP53) performed better or improved on lead-time. Our population study suggests that longitudinal HE4, CA72-4, anti-TP53 autoantibodies adds little value to longitudinal serum CA125 as a first-line test in ovarian cancer screening of postmenopausal women.

Fibroblasts from Distinct Pancreatic Pathologies Exhibit Disease-Specific Properties.

Although fibrotic stroma forms an integral component of pancreatic diseases, whether fibroblasts programmed by different types of pancreatic diseases are phenotypically distinct remains unknown. Here, we show that fibroblasts isolated from patients with pancreatic ductal adenocarcinoma (PDAC), chronic pancreatitis (CP), periampullary tumors, and adjacent normal (NA) tissue (N = 34) have distinct mRNA and miRNA profiles. Compared with NA fibroblasts, PDAC-associated fibroblasts were generally less sensitive to an antifibrotic stimulus (NPPB) and more responsive to positive regulators of activation such as TGFß1 and WNT. Of the disease-associated fibroblasts examined, PDAC- and CP-derived fibroblasts shared greatest similarity, yet PDAC-associated fibroblasts expressed higher levels of tenascin C (TNC), a finding attributable to miR-137, a novel regulator of TNC. TNC protein and transcript levels were higher in PDAC tissue versus CP tissue and were associated with greater levels of stromal activation, and conditioned media from TNC-depleted PDAC-associated fibroblasts modestly increased both PDAC cell proliferation and PDAC cell migration, indicating that stromal TNC may have inhibitory effects on PDAC cells. Finally, circulating TNC levels were higher in patients with PDAC compared with CP. Our characterization of pancreatic fibroblast programming as disease-specific has consequences for therapeutic targeting and for the manner in which fibroblasts are used in research. SIGNIFICANCE: Primary fibroblasts derived from various types of pancreatic diseases possess and retain distinct molecular and functional characteristics in culture, providing a series of cellular models for treatment development and disease-specific research.

Small extracellular vesicles secreted from human amniotic fluid mesenchymal stromal cells possess cardioprotective and promigratory potential.

Mesenchymal stromal cells (MSCs) exhibit antiapoptotic and proangiogenic functions in models of myocardial infarction which may be mediated by secreted small extracellular vesicles (sEVs). However, MSCs have frequently been harvested from aged or diseased patients, while the isolated sEVs often contain high levels of impurities. Here, we studied the cardioprotective and proangiogenic activities of size-exclusion chromatography-purified sEVs secreted from human foetal amniotic fluid stem cells (SS-hAFSCs), possessing superior functional potential to that of adult MSCs. We demonstrated for the first time that highly pure (up to 1.7 × 1010 particles/µg protein) and thoroughly characterised SS-hAFSC sEVs protect rat hearts from ischaemia-reperfusion injury in vivo when administered intravenously prior to reperfusion (38 ± 9% infarct size reduction, p < 0.05). SS-hAFSC sEVs did not protect isolated primary cardiomyocytes in models of simulated ischaemia-reperfusion injury in vitro, indicative of indirect cardioprotective effects. SS-hAFSC sEVs were not proangiogenic in vitro, although they markedly stimulated endothelial cell migration. Additionally, sEVs were entirely responsible for the promigratory effects of the medium conditioned by SS-hAFSC. Mechanistically, sEV-induced chemotaxis involved phosphatidylinositol 3-kinase (PI3K) signalling, as its pharmacological inhibition in treated endothelial cells reduced migration by 54 ± 7% (p < 0.001). Together, these data indicate that SS-hAFSC sEVs have multifactorial beneficial effects in a myocardial infarction setting.

Improved early detection of ovarian cancer using longitudinal multimarker models.

BACKGROUND: Ovarian cancer has a poor survival rate due to late diagnosis and improved methods are needed for its early detection. Our primary objective was to identify and incorporate additional biomarkers into longitudinal models to improve on the performance of CA125 as a first-line screening test for ovarian cancer. METHODS: This case-control study nested within UKCTOCS used 490 serial serum samples from 49 women later diagnosed with ovarian cancer and 31 control women who were cancer-free. Proteomics-based biomarker discovery was carried out using pooled samples and selected candidates, including those from the literature, assayed in all serial samples. Multimarker longitudinal models were derived and tested against CA125 for early detection of ovarian cancer. RESULTS: The best performing models, incorporating CA125, HE4, CHI3L1, PEBP4 and/or AGR2, provided 85.7% sensitivity at 95.4% specificity up to 1 year before diagnosis, significantly improving on CA125 alone. For Type II cases (mostly high-grade serous), models achieved 95.5% sensitivity at 95.4% specificity. Predictive values were elevated earlier than CA125, showing the potential of models to improve lead time. CONCLUSIONS: We have identified candidate biomarkers and tested longitudinal multimarker models that significantly improve on CA125 for early detection of ovarian cancer. These models now warrant independent validation.

Association between the cervicovaginal microbiome, BRCA1 mutation status, and risk of ovarian cancer: a case-control study.

BACKGROUND: Various factors-including age, family history, inflammation, reproductive factors, and tubal ligation-modulate the risk of ovarian cancer. In this study, our aim was to establish whether women with, or at risk of developing, ovarian cancer have an imbalanced cervicovaginal microbiome. METHODS: We did a case-control study in two sets of women aged 18-87 years in the Czech Republic, Germany, Italy, Norway, and the UK. The ovarian cancer set comprised women with epithelial ovarian cancer and controls (both healthy controls and those diagnosed with benign gynaecological conditions). The BRCA set comprised women with a BRCA1 mutation but without ovarian cancer and controls who were wild type for BRCA1 and BRCA2 (both healthy controls and those with benign gynaecological conditions). Cervicovaginal samples were gathered from all participants with the ThinPrep system and then underwent 16S rRNA gene sequencing. For each sample, we calculated the proportion of lactobacilli species (ie, Lactobacillus crispatus, Lactobacillus iners, Lactobacillus gasseri, and Lactobacillus jensenii), which are essential for the generation of a protective low vaginal pH, in the cervicovaginal microbiota. We grouped samples into those in which lactobacilli accounted for at least 50% of the species present (community type L) and those in which lactobacilli accounted for less than 50% of the species present (community type O). We assessed the adjusted association between BRCA1 status and ovarian cancer status and cervicovaginal microbiota community type, using a logistic regression model with a bias reduction method. FINDINGS: Participants were recruited between Jan 2, 2016, and July 21, 2018. The ovarian cancer set (n=360) comprised 176 women with epithelial ovarian cancer, 115 healthy controls and 69 controls with benign gynaecological conditions. The BRCA set (n=220) included 109 women with BRCA1 mutations, 97 healthy controls wild type for BRCA1 and BRCA2 and 14 controls with a benign gynaecological condition wild type for BRCA1 and BRCA2. On the basis of two-dimensional density plots, receiver-operating characteristic curve analysis, and age thresholds used previously, we divided the cohort into those younger than 50 years and those aged 50 years or older. In the ovarian cancer set, women aged 50 years or older had a higher prevalence of community type O microbiota (81 [61%] of 133 ovarian cancer cases and 84 [59%] of 142 healthy controls) than those younger than 50 years (23 [53%] of 43 cases and 12 [29%] of 42 controls). In the ovarian cancer set, women younger than 50 years with ovarian cancer had a significantly higher prevalence of community type O microbiota than did age-matched controls under a logistic regression model with bias correction (odds ratio [OR] 2·80 [95% CI 1·17-6·94]; p=0·020). In the BRCA set, women with BRCA1 mutations younger than 50 years were also more likely to have community type O microbiota than age-matched controls (OR 2·79 [95% CI 1·25-6·68]; p=0·012), after adjustment for pregnancy (ever). This risk was increased further if more than one first-degree family member was affected by any cancer (OR 5·26 [95% CI 1·83-15·30]; p=0·0022). In both sets, we noted that the younger the participants, the stronger the association between community type O microbiota and ovarian cancer or BRCA1 mutation status (eg, OR for community type O for cases aged <40 years in the ovarian cancer set 7·00 [95% CI 1·27-51·44], p=0·025; OR for community type O for BRCA1 mutation carriers aged <35 years in the BRCA set 4·40 [1·14-24·36], p=0·031). INTERPRETATION: The presence of ovarian cancer, or factors known to affect risk for the disease (ie, age and BRCA1 germline mutations), were significantly associated with having a community type O cervicovaginal microbiota. Whether re-instatement of a community type L microbiome by using, for example, vaginal suppositories containing live lactobacilli, would alter the microbiomial composition higher up in the female genital tract and in the fallopian tubes (the site of origin of high-grade serous ovarian cancer), and whether such changes could translate into a reduced incidence of ovarian cancer, needs to be investigated. FUNDING: EU Horizon 2020 Research and Innovation Programme, EU Horizon 2020 European Research Council Programme, and The Eve Appeal.

Discovery of non-invasive biomarkers for the diagnosis of endometriosis.

BACKGROUND: Endometriosis is a common gynaecological disorder affecting 5-10% of women of reproductive age who often experience chronic pelvic pain and infertility. Definitive diagnosis is through laparoscopy, exposing patients to potentially serious complications, and is often delayed. Non-invasive biomarkers are urgently required to accelerate diagnosis and for triaging potential patients for surgery. METHODS: This retrospective case control biomarker discovery and validation study used quantitative 2D-difference gel electrophoresis and tandem mass tagging-liquid chromatography-tandem mass spectrometry for protein expression profiling of eutopic and ectopic endometrial tissue samples collected from 28 cases of endometriosis and 18 control patients undergoing surgery for investigation of chronic pelvic pain without endometriosis or prophylactic surgery. Samples were further sub-grouped by menstrual cycle phase. Selected differentially expressed candidate markers (LUM, CPM, TNC, TPM2 and PAEP) were verified by ELISA in a set of 87 serum samples collected from the same and additional women. Previously reported biomarkers (CA125, sICAM1, FST, VEGF, MCP1, MIF and IL1R2) were also validated and diagnostic performance of markers and combinations established. RESULTS: Cycle phase and endometriosis-associated proteomic changes were identified in eutopic tissue from over 1400 identified gene products, yielding potential biomarker candidates. Bioinformatics analysis revealed enrichment of adhesion/extracellular matrix proteins and progesterone signalling. The best single marker for discriminating endometriosis from controls remained CA125 (AUC = 0.63), with the best cross-validated multimarker models improving the AUC to 0.71-0.81, depending upon menstrual cycle phase and control group. CONCLUSIONS: We have identified menstrual cycle- and endometriosis-associated protein changes linked to various cellular processes that are potential biomarkers and that provide insight into the biology of endometriosis. Our data indicate that the markers tested, whilst not useful alone, have improved diagnostic accuracy when used in combination and demonstrate menstrual cycle specificity. Tissue heterogeneity and blood contamination is likely to have hindered biomarker discovery, whilst a small sample size precludes accurate determination of performance by cycle phase. Independent validation of these biomarker panels in a larger cohort is however warranted, and if successful, they may have clinical utility in triaging patients for surgery.

Colorectal cancer ascertainment through cancer registries, hospital episode statistics, and self-reporting compared to confirmation by clinician: A cohort study nested within the UK Collaborative Trial of Ovarian Cancer Screening (UKCTOCS).

BACKGROUND: Electronic health records are frequently used for cancer epidemiology. We report on their quality for ascertaining colorectal cancer (CRC) in UK women. METHODS: Population-based, retrospective cohort study nested within the UK Collaborative Trial of Ovarian Cancer Screening (UKCTOCS). Postmenopausal women aged 50-74 who were diagnosed with CRC during 2001-11 following randomisation to the UKCTOCS were identified and their diagnosis confirmed with their treating clinician. The sensitivity and positive predictive value (PPV) of cancer and death registries, hospital episode statistics, and self-reporting were calculated by pairwise comparisons to the treating clinician's confirmation, while specificity and negative predictive value were estimated relative to expected cases. RESULTS: Notification of CRC events were received for 1,085 women as of 24 May 2011. Responses were received from 61% (660/1,085) of clinicians contacted. Nineteen women were excluded (18 no diagnosis date, one diagnosed after cut-off). Of the 641 eligible, 514 had CRC, 24 had a benign polyp, and 103 had neither diagnosis. The sensitivity of cancer registrations at one- and six-years post-diagnosis was 92 (95% CI 90-94) and 99% (97-100), respectively, with a PPV of 95% (95% CI 92/93-97). The sensitivity & PPV of cancer registrations (at one-year post-diagnosis) & hospital episode statistics combined were 98 (96-99) and 92% (89-94), respectively. CONCLUSIONS: Cancer and death registrations in the UK are a reliable resource for CRC ascertainment in women. Hospital episode statistics can supplement delays in cancer registration. Self-reporting seems less reliable.

Comparison of Longitudinal CA125 Algorithms as a First-Line Screen for Ovarian Cancer in the General Population.

Purpose: In the United Kingdom Collaborative Trial of Ovarian Cancer Screening (UKCTOCS), women in the multimodal (MMS) arm had a serum CA125 test (first-line), with those at increased risk, having repeat CA125/ultrasound (second-line test). CA125 was interpreted using the "Risk of Ovarian Cancer Algorithm" (ROCA). We report on performance of other serial algorithms and a single CA125 threshold as a first-line screen in the UKCTOCS dataset.Experimental Design: 50,083 post-menopausal women who attended 346,806 MMS screens were randomly split into training and validation sets, following stratification into cases (ovarian/tubal/peritoneal cancers) and controls. The two longitudinal algorithms, a new serial algorithm, method of mean trends (MMT) and the parametric empirical Bayes (PEB) were trained in the training set and tested in the blinded validation set and the performance characteristics, including that of a single CA125 threshold, were compared.Results: The area under receiver operator curve (AUC) was significantly higher (P = 0.01) for MMT (0.921) compared with CA125 single threshold (0.884). At a specificity of 89.5%, sensitivities for MMT [86.5%; 95% confidence interval (CI), 78.4-91.9] and PEB (88.5%; 95% CI, 80.6-93.4) were similar to that reported for ROCA (sensitivity 87.1%; specificity 87.6%; AUC 0.915) and significantly higher than the single CA125 threshold (73.1%; 95% CI, 63.6-80.8).Conclusions: These findings from the largest available serial CA125 dataset in the general population provide definitive evidence that longitudinal algorithms are significantly superior to simple cutoff values for ovarian cancer screening. Use of these newer algorithms requires incorporation into a multimodal strategy. The results highlight the importance of incorporating serial change in biomarker levels in cancer screening/early detection strategies. Clin Cancer Res; 24(19); 4726-33. ©2018 AACR.

Parenclitic networks for predicting ovarian cancer.

Prediction and diagnosis of complex disease may not always be possible with a small number of biomarkers. Modern 'omics' technologies make it possible to cheaply and quantitatively assay hundreds of molecules generating large amounts of data from individual samples. In this study, we describe a parenclitic network-based approach to disease classification using a synthetic data set modelled on data from the United Kingdom Collaborative Trial of Ovarian Cancer Screening (UKCTOCS) and serological assay data from a nested set of samples from the same study. This approach allows us to integrate quantitative proteomic and categorical metadata into a single network, and then use network topologies to construct logistic regression models for disease classification. In this study of ovarian cancer, comprising of 30 controls and cases with samples taken <14 months to diagnosis (n = 30) and/or >34 months to diagnosis (n = 29), we were able to classify cases with a sensitivity of 80.3% within 14 months of diagnosis and 18.9% in samples exceeding 34 months to diagnosis at a specificity of 98%. Furthermore, we use the networks to make observations about proteins within the cohort and identify GZMH and FGFBP1 as changing in cases (in relation to controls) at time points most distal to diagnosis. We conclude that network-based approaches may offer a solution to the problem of complex disease classification that can be used in personalised medicine and to describe the underlying biology of cancer progression at a system level.

Identification of a serum biomarker panel for the differential diagnosis of cholangiocarcinoma and primary sclerosing cholangitis.

The non-invasive differentiation of malignant and benign biliary disease is a clinical challenge. Carbohydrate antigen 19-9 (CA19-9), leucine-rich α2-glycoprotein (LRG1), interleukin 6 (IL6), pyruvate kinase M2 (PKM2), cytokeratin 19 fragment (CYFRA21.1) and mucin 5AC (MUC5AC) have reported utility for differentiating cholangiocarcinoma (CCA) from benign biliary disease. Herein, serum levels of these markers were tested in 66 cases of CCA and 62 cases of primary sclerosing cholangitis (PSC) and compared with markers of liver function and inflammation. Markers panels were assessed for their ability to discriminate malignant and benign disease. Several of the markers were also assessed in pre-diagnosis biliary tract cancer (BTC) samples with performances evaluated at different times prior to diagnosis. We show that LRG1 and IL6 were unable to accurately distinguish CCA from PSC, whereas CA19-9, PKM2, CYFRA21.1 and MUC5AC were significantly elevated in malignancy. Area under the receiver operating characteristic curves for these individual markers ranged from 0.73-0.84, with the best single marker (PKM2) providing 61% sensitivity at 90% specificity. A panel combining PKM2, CYFRA21.1 and MUC5AC gave 76% sensitivity at 90% specificity, which increased to 82% sensitivity by adding gamma-glutamyltransferase (GGT). In the pre-diagnosis setting, LRG1, IL6 and PKM2 were poor predictors of BTC, whilst CA19-9 and C-reactive protein were elevated up to 2 years before diagnosis. In conclusion, LRG1, IL6 and PKM2 were not useful for early detection of BTC, whilst a model combining PKM2, CYFRA21.1, MUC5AC and GGT was beneficial in differentiating malignant from benign biliary disease, warranting validation in a prospective trial.

Evidence against Stable Protein S-Nitrosylation as a Widespread Mechanism of Post-translational Regulation.

S-nitrosation, commonly referred to as S-nitrosylation, is widely regarded as a ubiquitous, stable post-translational modification that directly regulates many proteins. Such a widespread role would appear to be incompatible with the inherent lability of the S-nitroso bond, especially its propensity to rapidly react with thiols to generate disulfide bonds. As anticipated, we observed robust and widespread protein S-nitrosation after exposing cells to nitrosocysteine or lipopolysaccharide. Proteins detected using the ascorbate-dependent biotin switch method are typically interpreted to be directly regulated by S-nitrosation. However, these S-nitrosated proteins are shown to predominantly comprise transient intermediates leading to disulfide bond formation. These disulfides are likely to be the dominant end effectors resulting from elevations in nitrosating cellular nitric oxide species. We propose that S-nitrosation primarily serves as a transient intermediate leading to disulfide formation. Overall, we conclude that the current widely held perception that stable S-nitrosation directly regulates the function of many proteins is significantly incorrect.

Evidence of Altered Glycosylation of Serum Proteins Prior to Pancreatic Cancer Diagnosis.

Biomarkers for the early detection of pancreatic cancer are urgently needed. The aim of this pilot study was to evaluate changes in serum N-glycoproteins and their glycosylation status prior to clinical presentation of pancreatic cancer that may be potential biomarkers. Prediagnosis serum samples pooled according to five time-to-diagnosis groups and a non-cancer control pool were digested with trypsin, labelled with mass tags, and subjected to titanium dioxide capture, deglycosylation, and 2D-LC-MS/MS profiling. Unbound peptides were profiled in parallel. Across the sample groups, 703 proteins were quantified and 426 putative sites of N-glycosylation were identified with evidence of several novel sites. Altered proteins with biomarker potential were predominantly abundant inflammatory response, coagulation, and immune-related proteins. Whilst glycopeptide profiles largely paralleled those of their parent proteins, there was evidence of altered N-glycosylation site occupancy or sialic acid content prior to diagnosis for some proteins, most notably of immunoglobulin gamma chains. α-1-Antitrypsin was tested as a biomarker, but found not to complement carbohydrate antigen 19-9 (CA19-9) in early detection of cancer. In conclusion, we provide preliminary evidence of altered glycosylation of several serum proteins prior to pancreatic cancer diagnosis, warranting further investigation of these proteins as early biomarkers. These changes may be largely driven by inflammatory processes that occur in response to tumour formation and progression.

Effects of ErbB2 Overexpression on the Proteome and ErbB Ligand-specific Phosphosignaling in Mammary Luminal Epithelial Cells.

Most breast cancers arise from luminal epithelial cells, and 25-30% of these tumors overexpress the ErbB2/HER2 receptor that correlates with disease progression and poor prognosis. The mechanisms of ErbB2 signaling and the effects of its overexpression are not fully understood. Herein, stable isotope labeling by amino acids in cell culture (SILAC), expression profiling, and phosphopeptide enrichment of a relevant, non-transformed, and immortalized human mammary luminal epithelial cell model were used to profile ErbB2-dependent differences in protein expression and phosphorylation events triggered via EGF receptor (EGF treatment) and ErbB3 (HRG1ß treatment) in the context of ErbB2 overexpression. Bioinformatics analysis was used to infer changes in cellular processes and signaling events. We demonstrate the complexity of the responses to oncogene expression and growth factor signaling, and we identify protein changes relevant to ErbB2-dependent altered cellular phenotype, in particular cell cycle progression and hyper-proliferation, reduced adhesion, and enhanced motility. Moreover, we define a novel mechanism by which ErbB signaling suppresses basal interferon signaling that would promote the survival and proliferation of mammary luminal epithelial cells. Numerous novel sites of growth factor-regulated phosphorylation were identified that were enhanced by ErbB2 overexpression, and we putatively link these to altered cell behavior and also highlight the importance of performing parallel protein expression profiling alongside phosphoproteomic analysis.

Testing breast cancer serum biomarkers for early detection and prognosis in pre-diagnosis samples.

BACKGROUND: Breast cancer is a leading cause of morbidity and mortality worldwide. Although mammography screening is available, there is an ongoing interest in improved early detection and prognosis. Herein, we have analysed a combination of serological biomarkers in a case-control cohort of sera taken before diagnosis. METHODS: This nested case-control study within the UK Collaborative Trial of Ovarian Cancer Screening (UKCTOCS) used serum samples from 239 women who subsequently developed breast cancer and 239 matched cancer-free controls. Sera were screened by ELISA for 9 candidate markers. Univariate and multivariate analyses were performed to examine associations with clinico-pathological features and between case controls in different time groups before diagnosis. RESULTS: Significant associations with clinico-pathological features related to prognosis were found for several candidates (CA15-3, HSP90A and PAI-1). However, there were no consistent differences between cases and controls for any candidate in the lead up to diagnosis. Whilst combination models outperformed single markers, there was no increase in performance towards diagnosis. CONCLUSIONS: This study using unique pre-diagnosis samples shows that CA15-3, HSP90A and PAI-1 have potential as early prognostic markers and warrant further investigation. However, none of the candidates or combinations would be useful for screening.

Proteomic Analysis of Kveim Reagent Identifies Targets of Cellular Immunity in Sarcoidosis.

BACKGROUND: Kveim-reagent (Kv) skin testing was a historical method of diagnosing sarcoidosis. Intradermal injection of treated sarcoidosis spleen tissue resulted in a granuloma response at injection site by 4-6 weeks. Previous work indicates proteins as the possible trigger of this reaction. We aimed to identify Kv-specific proteins and characterise the ex vivo response of Peripheral Blood Mononuclear Cells (PBMCs) from sarcoidosis, tuberculosis and healthy control patients when stimulated with both Kv and selected Kv-specific proteins. METHODS: Kv extracts were separated by 1D-SDS-PAGE and 2D-DIGE and then underwent mass spectrometric analysis for protein identification. Sarcoidosis and control PBMCs were first stimulated with Kv and then with three selected recombinant protein candidates which were identified from the proteomic analysis. PBMC secreted cytokines were subsequently measured by Multiplex Cytokine Assay. RESULTS: We observed significantly increased IFN-γ and TNF-α secretion from Kv-stimulated PBMCs of sarcoidosis patients vs. PBMCs from healthy volunteers (IFN-γ: 207.2 pg/mL vs. 3.86 pg/mL, p = 0.0018; TNF-α: 2375 pg/mL vs. 42.82 pg/mL, p = 0.0003). Through proteomic approaches we then identified 74 sarcoidosis tissue-specific proteins. Of these, 3 proteins (vimentin, tubulin and alpha-actinin-4) were identified using both 1D-SDS-PAGE and 2D-DIGE. Data are available via ProteomeXchange with identifier PXD005150. Increased cytokine secretion was subsequently observed with vimentin stimulation of sarcoidosis PBMCs vs. tuberculosis PBMCs (IFN-γ: 396.6 pg/mL vs 0.1 pg/mL, p = 0.0009; TNF-α: 1139 pg/mL vs 0.1 pg/mL, p<0.0001). This finding was also observed in vimentin stimulation of sarcoidosis PBMCs compared to PBMCs from healthy controls (IFN-γ: 396.6 pg/mL vs. 0.1 pg/mL, p = 0.014; TNF-α: 1139 pg/mL vs 42.29 pg/mL, p = 0.027). No difference was found in cytokine secretion between sarcoidosis and control PBMCs when stimulated with either tubulin or alpha-actinin-4. CONCLUSIONS: Stimulation with both Kveim reagent and vimentin induces a specific pro-inflammatory cytokine secretion from sarcoidosis PBMCs. Further investigation of cellular immune responses to Kveim-specific proteins may identify novel biomarkers to assist the diagnosis of sarcoidosis.

EVERREST prospective study: a 6-year prospective study to define the clinical and biological characteristics of pregnancies affected by severe early onset fetal growth restriction.

BACKGROUND: Fetal growth restriction (FGR) is a serious obstetric condition for which there is currently no treatment. The EVERREST Prospective Study has been designed to characterise the natural history of pregnancies affected by severe early onset FGR and establish a well phenotyped bio-bank. The findings will provide up-to-date information for clinicians and patients and inform the design and conduct of the EVERREST Clinical Trial: a phase I/IIa trial to assess the safety and efficacy of maternal vascular endothelial growth factor (VEGF) gene therapy in severe early onset FGR. Data and samples from the EVERREST Prospective Study will be used to identify ultrasound and/or biochemical markers of prognosis in pregnancies with an estimated fetal weight (EFW) <3rd centile between 20+0 and 26+6 weeks of gestation. METHODS: This is a 6 year European multicentre prospective cohort study, recruiting women with a singleton pregnancy where the EFW is <3rd centile for gestational age and <600 g at 20+0 to 26+6 weeks of gestation. Detailed data are collected on: maternal history; antenatal, peripartum, and postnatal maternal complications; health economic impact; psychological impact; neonatal condition, progress and complications; and infant growth and neurodevelopment to 2 years of corrected age in surviving infants. Standardised longitudinal ultrasound measurements are performed, including: fetal biometry; uterine artery, umbilical artery, middle cerebral artery, and ductus venosus Doppler velocimetry; and uterine artery and umbilical vein volume blood flow. Samples of maternal blood and urine, amniotic fluid (if amniocentesis performed), placenta, umbilical cord blood, and placental bed (if caesarean delivery performed) are collected for bio-banking. An initial analysis of maternal blood samples at enrolment is planned to identify biochemical markers that are predictors for fetal or neonatal death. DISCUSSION: The findings of the EVERREST Prospective Study will support the development of a novel therapy for severe early onset FGR by describing in detail the natural history of the disease and by identifying women whose pregnancies have the poorest outcomes, in whom a therapy might be most advantageous. The findings will also enable better counselling of couples with affected pregnancies, and provide a valuable resource for future research into the causes of FGR. TRIAL REGISTRATION: NCT02097667 registered 31st October 2013.